Assessment of mental workload based on multi-physiological signals

Xiaoli Fan · Chaoyi Zhao · Xin Zhang · Hong Luo · Wei Zhang

61 citations0/0 files storedTechnology and Health Care

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Human-Automation Interaction and Safety EEG and Brain-Computer Interfaces Emotion and Mood Recognition

Abstract

BACKGROUND: Mental workload is one of the contributing factors to human errors in road accidents or other potentially adverse incidents. OBJECTIVE: This research probes the effects of mental workload on the electroencephalographic (EEG) and electrocardiogram (ECG) of subjects in visual monitoring tasks, based on which a comprehensive evaluation model for mental workload is established effectively. METHODS: Three degrees of mental workload were obtained by monitoring tasks with different levels of difficulty. 20 healthy subjects were selected to take part in the research. RESULTS: The subjective scores showed a significant increase with the increase of task difficulty, meanwhile the reaction time (RT) increased and the accuracy decreased significantly, which proved the validity of three degrees of mental workload induced. For the EEG parameters, a significant decrease of θ energy was found in Frontal, Parietal and Occipital with the increase of level of mental workload, as well as a significant decrease of α energy in Frontal, Central and Occipital, meanwhile a significant increase of β energy occurred in Frontal and Occipital. There was a significant decrease of α/θ in Occipital, and significant increases of θ/β and (α+β)/θ in Frontal, Central and Occipital, meanwhile (α+θ)/β and WPE decreased significantly in Frontal and Occipital. Among the ECG parameters, it was shown that Mean RR, RMSSD, HF_norm and SampEn decreased significantly with the increase of task difficulty, while LF_norm and LF/HF showed significant increases. These EEG indictors in Occipital and ECG indictors were chosen and constituted a multidimensional original sample. Principal Component Analysis (PCA) was used to extract the principal elements and decreased the dimension of sample space in order to simplify the calculation, based on which an effective classification model with accuracy of 80% was achieved by support vector machine (SVM). CONCLUSION: This study demonstrates that the proposed algorithm can be applied to mental workload monitoring.

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References

1988Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research 1988The Subjective Workload Assessment Technique: A Scaling Procedure for Measuring Mental Workload 1994Measurement of Cognitive Load in Instructional Research 2002An Analysis of Mental Workload in Pilots During Flight Using Multiple Psychophysiological Measures 2009Impact of Incremental Increases in Cognitive Workload on Physiological Arousal and Performance in Young Adult Drivers 2014Is the frontal lobe involved in conscious perception?2014Mental workload and driving 2012Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices

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